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Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures

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  • Seo, Minhwan
  • Song, Youngbin
  • Kim, Jake
  • Paek, Sung Wook
  • Kim, Gi-Heon
  • Kim, Sang Woo

Abstract

Ambient temperature alters properties of lithium-ion batteries and affects the accuracy of estimation of state of charge (SOC), which is an important function to ensure the safety and reliability of electric vehicles. An accurate SOC estimation is critical under various temperatures. The existing methods have two problems: 1) need to perform time-consuming pre-experiments to investigate influence of various temperatures on the battery properties, and 2) use of the Thevenin model which is inaccurate at sub-zero temperatures. This study proposes an innovative lumped-battery model to improve the accuracy of both SOC estimation and battery modeling without pre-experiments. Two main causes of modeling errors by the Thevenin model are analyzed. Proposed model parameters are estimated using the recursive least squares, and the extended Kalman filter is used to estimate SOC in real time. Experiments are conducted under time-invariant and time-varying temperature conditions ranging from −10 °C to 30 °C. The results indicate that relative errors of battery modeling are less than 2.4% and that estimation errors of SOC are at most 0.4% under various temperatures. Therefore, the proposed method can be conveniently and widely applied to all-climate battery management systems to achieve a high accuracy of SOC estimation.

Suggested Citation

  • Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221005508
    DOI: 10.1016/j.energy.2021.120301
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    4. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    5. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    6. Fan, Kesen & Wan, Yiming & Wang, Zhuo & Jiang, Kai, 2023. "Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process," Energy, Elsevier, vol. 268(C).
    7. Yang, Kuo & Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2022. "A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism," Energy, Elsevier, vol. 244(PB).
    8. Peng Guo & Xiaobo Wu & António M. Lopes & Anyu Cheng & Yang Xu & Liping Chen, 2022. "Parameter Identification for Lithium-Ion Battery Based on Hybrid Genetic–Fractional Beetle Swarm Optimization Method," Mathematics, MDPI, vol. 10(17), pages 1-11, August.
    9. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).

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